Microsoft의 에이전트 프레임워크를 사용하여 다중 에이전트 AI 시스템 구축

ChatCompletionAgent agent = new()
{
    Name = "Reviewer",
    Instructions = "You are a senior code reviewer. Analyze the provided code for bugs, security issues, and style violations. Be concise and actionable.",
    Kernel = kernel
};

OpenAIAssistantAgent agent = await OpenAIAssistantAgent.CreateAsync(
    clientProvider: clientProvider,
    definition: new OpenAIAssistantDefinition("gpt-4o")
    {
        Name = "DataAnalyst",
        Instructions = "You analyze datasets and produce statistical summaries."
    }
);

// Conceptual flow
var draft = await writerAgent.InvokeAsync("Write a blog post about .NET 9");
var reviewed = await reviewerAgent.InvokeAsync($"Review this: {draft}");
var edited = await editorAgent.InvokeAsync($"Edit based on feedback: {reviewed}");

dotnet new console -n AgentDemo
cd AgentDemo
dotnet add package Microsoft.SemanticKernel --prerelease
dotnet add package Microsoft.SemanticKernel.Agents.Core --prerelease
dotnet add package Azure.AI.OpenAI
dotnet add package Azure.Identity

using Azure.Identity;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;
using Microsoft.SemanticKernel.ChatCompletion;

var builder = Kernel.CreateBuilder();

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4o",
    endpoint: "https://your-resource.openai.azure.com/",
    credentials: new DefaultAzureCredential()
);

Kernel kernel = builder.Build();

ChatCompletionAgent agent = new()
{
    Name = "TechWriter",
    Instructions = """
        You are a technical writer specializing in software documentation.
        Write clear, concise content aimed at experienced developers.
        Use code examples when appropriate.
        Always structure your output with headings and bullet points.
        """,
    Kernel = kernel
};

ChatHistory history = new();
history.AddUserMessage("Explain dependency injection in .NET in 200 words.");

await foreach (ChatMessageContent response in agent.InvokeAsync(history))
{
    Console.WriteLine(response.Content);
}

ChatCompletionAgent writer = new()
{
    Name = "Writer",
    Instructions = """
        You are a content writer. When given a topic, produce a well-structured
        draft. Focus on clarity and technical accuracy.
        When you receive feedback from the Reviewer, incorporate it into a
        revised draft.
        """,
    Kernel = kernel
};

ChatCompletionAgent reviewer = new()
{
    Name = "Reviewer",
    Instructions = """
        You are a content reviewer. Analyze drafts for technical accuracy,
        clarity, and completeness. Provide specific, actionable feedback.
        Do NOT rewrite the content — only provide feedback.
        When the content meets your standards, respond with: APPROVED
        """,
    Kernel = kernel
};

ChatCompletionAgent editor = new()
{
    Name = "Editor",
    Instructions = """
        You are an editor. Once content is approved by the Reviewer,
        polish it for grammar, tone, and formatting.
        Output only the final polished version.
        When you have produced the final version, respond with: COMPLETE
        """,
    Kernel = kernel
};

AgentGroupChat chat = new(writer, reviewer, editor)
{
    ExecutionSettings = new()
    {
        SelectionStrategy = new SequentialSelectionStrategy(),
        TerminationStrategy = new ApprovalTerminationStrategy()
        {
            Agents = [editor],
            MaximumIterations = 12
        }
    }
};

class ApprovalTerminationStrategy : TerminationStrategy
{
    protected override Task<bool> ShouldAgentTerminateAsync(
        Agent agent,
        IReadOnlyList<ChatMessageContent> history,
        CancellationToken cancellationToken = default)
    {
        bool isComplete = history
            .Last()
            .Content?
            .Contains("COMPLETE", StringComparison.OrdinalIgnoreCase) ?? false;

        return Task.FromResult(isComplete);
    }
}

chat.AddChatMessage(
    new ChatMessageContent(AuthorRole.User,
        "Write a 300-word technical overview of gRPC vs REST for microservices.")
);

await foreach (ChatMessageContent message in chat.InvokeAsync())
{
    Console.WriteLine($"[{message.AuthorName}]: {message.Content}");
    Console.WriteLine("---");
}

public class ContentToolsPlugin
{
    [KernelFunction("word_count")]
    [Description("Counts the number of words in the provided text.")]
    public int WordCount([Description("The text to count words in")] string text)
    {
        return text.Split(' ', StringSplitOptions.RemoveEmptyEntries).Length;
    }

    [KernelFunction("check_readability")]
    [Description("Calculates a readability score for the given text.")]
    public string CheckReadability([Description("The text to analyze")] string text)
    {
        var words = text.Split(' ', StringSplitOptions.RemoveEmptyEntries).Length;
        var sentences = text.Split(['.', '!', '?'], StringSplitOptions.RemoveEmptyEntries).Length;

        if (sentences == 0) return "Unable to calculate — no sentences found.";

        double avgWordsPerSentence = (double)words / sentences;

        return avgWordsPerSentence switch
        {
            < 15 => $"Easy to read (avg {avgWordsPerSentence:F1} words/sentence)",
            < 25 => $"Moderate difficulty (avg {avgWordsPerSentence:F1} words/sentence)",
            _ => $"Difficult to read (avg {avgWordsPerSentence:F1} words/sentence). Consider shorter sentences."
        };
    }
}

kernel.Plugins.AddFromType<ContentToolsPlugin>();

ChatCompletionAgent analyst = new()
{
    Name = "ContentAnalyst",
    Instructions = "Analyze content using available tools. Report word count and readability.",
    Kernel = kernel,
    Arguments = new KernelArguments(
        new PromptExecutionSettings
        {
            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
        })
};

// Example: Adding an MCP server for file operations
kernel.Plugins.AddFromMcpServer("filesystem",
    new Uri("http://localhost:3000/mcp"));

using Azure.Identity;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;
using Microsoft.SemanticKernel.ChatCompletion;

// Build the kernel
var builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4o",
    endpoint: "https://your-resource.openai.azure.com/",
    credentials: new DefaultAzureCredential()
);
Kernel kernel = builder.Build();

// Define specialized agents
ChatCompletionAgent researcher = new()
{
    Name = "Researcher",
    Instructions = """
        You are a technical researcher. Given a topic, identify the key
        concepts, recent developments, and important details that should
        be covered. Output a structured research brief.
        """,
    Kernel = kernel
};

ChatCompletionAgent writer = new()
{
    Name = "Writer",
    Instructions = """
        You are a technical writer. Using the research brief provided,
        write a comprehensive, well-structured article. Include code
        examples where relevant. Target audience: experienced developers.
        """,
    Kernel = kernel
};

ChatCompletionAgent reviewer = new()
{
    Name = "Reviewer",
    Instructions = """
        You are a senior technical reviewer. Check the article for:
        - Technical accuracy
        - Completeness relative to the research brief
        - Code correctness
        - Clarity and structure
        If everything looks good, respond with APPROVED.
        Otherwise, provide specific feedback for revision.
        """,
    Kernel = kernel
};

ChatCompletionAgent publisher = new()
{
    Name = "Publisher",
    Instructions = """
        You are a content publisher. Once the article is approved,
        format it with proper markdown, add a summary at the top,
        and ensure consistent formatting throughout.
        End your response with COMPLETE.
        """,
    Kernel = kernel
};

// Configure the group chat
AgentGroupChat chat = new(researcher, writer, reviewer, publisher)
{
    ExecutionSettings = new()
    {
        SelectionStrategy = new SequentialSelectionStrategy(),
        TerminationStrategy = new ApprovalTerminationStrategy()
        {
            Agents = [publisher],
            MaximumIterations = 15
        }
    }
};

// Start the pipeline
chat.AddChatMessage(new ChatMessageContent(
    AuthorRole.User,
    "Create a technical article about implementing health checks in ASP.NET Core microservices."
));

await foreach (ChatMessageContent message in chat.InvokeAsync())
{
    Console.ForegroundColor = message.AuthorName switch
    {
        "Researcher" => ConsoleColor.Cyan,
        "Writer" => ConsoleColor.Green,
        "Reviewer" => ConsoleColor.Yellow,
        "Publisher" => ConsoleColor.Magenta,
        _ => ConsoleColor.White
    };

    Console.WriteLine($"\n{'='new string('=', 60)}");
    Console.WriteLine($"[{message.AuthorName}]");
    Console.WriteLine(new string('=', 60));
    Console.WriteLine(message.Content);
}

Console.ResetColor();

TerminationStrategy = new ApprovalTerminationStrategy()
{
    MaximumIterations = 12 // Safety net
}

try
{
    await foreach (var message in chat.InvokeAsync(cancellationToken))
    {
        // Process messages
    }
}
catch (HttpOperationException ex) when (ex.StatusCode == System.Net.HttpStatusCode.TooManyRequests)
{
    // Implement retry with exponential backoff
    await Task.Delay(TimeSpan.FromSeconds(30), cancellationToken);
}

builder.Services.AddOpenTelemetry()
    .WithTracing(tracing =>
    {
        tracing.AddSource("Microsoft.SemanticKernel*");
        tracing.AddOtlpExporter();
    });